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| /** | |
| * Instagram Comment Auto-Deletion Script | |
| * original by sbolel, changes made by braunglasrakete in Apr 2025, updates made by attaxika in Oct 2025 | |
| * | |
| * important: UI language must be set to English for the script to work | |
| * | |
| * WARNING: This function directly manipulates the DOM and depends on the current HTML | |
| * structure of Instagram's website to work. If Instagram implements changes to the | |
| * activity page layout, structure, or functionality, this script may break or cause | |
| * unexpected behavior. Use at your own risk and always review code before running it. |
A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory 20K+ Stars ⭐️, a persistent memory engine for AI coding agents.
This builds on Andrej Karpathy's original LLM Wiki idea file. Everything in the original still applies. This document adds what we learned running the pattern in production: what breaks at scale, what's missing, and what separates a wiki that stays useful from one that rots.
The core insight is correct: stop re-deriving, start compiling. RAG retrieves and forgets. A wiki accumulates and compounds. The three-layer architecture (raw sources, wiki, schema) works. The operations (ingest, query, lint) cover the basics. If you haven't read the original, start there.
| { | |
| "com.8bit.bitwarden": "bitwarden://", | |
| "com.apple.airport.mobileairportutility": "apmanage://", | |
| "com.apple.appleseed.FeedbackAssistant": "applefeedback://", | |
| "com.apple.AppStore": "itms-apps://itunes.apple.com/", | |
| "com.apple.AppStoreConnect": "shortcuts://run-shortcut?name=Icon%20Themer&input=%7B%22launch%22%3A%22Connect%22%7D", | |
| "com.apple.artistconnect": "shortcuts://run-shortcut?name=Icon%20Themer&input=%7B%22launch%22%3A%22Artists%22%7D", | |
| "com.apple.bnd": "beatsbond://", | |
| "com.apple.Bridge": "com.apple.bridge://x", | |
| "com.apple.calculator": "shortcuts://x-callback-url/run-shortcut?x-error=calc://", |
| Latency Comparison Numbers (~2012) | |
| ---------------------------------- | |
| L1 cache reference 0.5 ns | |
| Branch mispredict 5 ns | |
| L2 cache reference 7 ns 14x L1 cache | |
| Mutex lock/unlock 25 ns | |
| Main memory reference 100 ns 20x L2 cache, 200x L1 cache | |
| Compress 1K bytes with Zippy 3,000 ns 3 us | |
| Send 1K bytes over 1 Gbps network 10,000 ns 10 us | |
| Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD |
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
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